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CVA and FVA Modeling and Implementation

  • Dongsheng Lu
Chapter
Part of the Financial Engineering Explained book series (FEX)

Abstract

CVA/FVA modeling is complex, mainly because:
  • The potential credit/funding exposures are generally non-linear. For a linear fixed-for-floating interest rate swap, one may value the swap based on discounting of cash flows; however, the CVA/FVA exposures would depend on the distribution of the interest rate, and it is really a swaption.

  • CVA/FVA must be calculated on a net basis. If you have done a thousand trades across different asset classes with one counterparty, you have to net all the future exposures while calculating the default loss or funding cost/benefit. This means you would need to have a consistent simulation framework for all the risk factors involved across different asset classes.

  • CVA/FVA calculations have to deal with complex CSAs, such as ratings-based threshold, and automatic/discretionary triggers/terminations, among other things. This means the rating migrations must be incorporated in the CVA/FVA calculations.

  • There may exist significant correlations between credit and market risk factors, which need to be accounted for in CVA/FVA calculations. This means the market risk factors and credit risk factors have to be consistently simulated.

  • For FVA, funding and collaterals are involved, which means multi-currency/asset collateral curves and collateral choice options must be dealt with, as well as the firm’s own funding curves. When asymmetric funding curve is used, one may have to deal with funding set and netting set issues.

  • There are other legal issues surrounding the enforceability of CSAs in different jurisdictions and authorities, which could introduce extra dimension of issues in calculating CVA properly.

Keywords

Graphic Processing Unit Credit Rating Default Probability Credit Spread Risk Neutral Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Chapter 7

  1. 1.
    See Corrado, C. and Su, T. (1996) “Skewness and Kurtosis in S&P 500 Index Returns Implied by Option Prices”, Journal of Financial Research 19: 175–192.CrossRefGoogle Scholar
  2. 5.
    See Leland, H. (1985) “Option Pricing and Replication with Transaction Cost”, Journal of Finance, 40: 1283–1302.CrossRefGoogle Scholar
  3. 6.
    See for example, Schweizer, M. (1991) “Option Hedging for Semimartingales”. Stochastic Processes and their Applications 37: 339–367;CrossRefGoogle Scholar
  4. Alexander, C. and Nogueira, L. (2007) “Model-free Hedge Ratios and Scale-Invariant Models”, Journal of Banking and Finance 31: 1839–1861.CrossRefGoogle Scholar

Copyright information

© Dongsheng Lu 2015

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  • Dongsheng Lu

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